OpenRaiser/Intern-Atlas huggingface dataset:
Analyzing and characterizing political discussions in WhatsApp public groups
Venue: arXiv cs.SI 2018
Year: 2018

We present a thorough characterization of what we believe to be the first significant analysis of the behavior of groups in WhatsApp in the scientific literature. Our characterization of over 270,000 messages and about 7,000 users spanning a 28-day period is done at three different layers. The message layer focuses on individual messages, each of which is the result of specific posts performed by a user. The user layer characterizes the user actions while interacting with a group. The group layer characterizes the aggregate message patterns of all users that participate in a group. We analyze 81 public groups in WhatsApp and classify them into two categories, political and non-political groups according to keywords associated with each group. Our contributions are two-fold. First, we introduce a framework and a number of metrics to characterize the behavior of communication groups in mobile messaging systems such as WhatsApp. Second, our analysis underscores a Zipf-like profile for user messages in political groups. Also, our analysis reveals that Whatsapp messages are multimedia, with a combination of different forms of content. Multimedia content (i.e., audio, image, and video) and emojis are present in 20% and 11.2% of all messages respectively. Political groups use more text messages than non-political groups. Second, we characterize novel features that represent the behavior of a public group, with multiple conversational turns between key members, with the participation of other members of the group.
Application of Grey Numbers to Assessment Processes
Venue: arXiv cs.AI 2018
Year: 2018

The theory of grey systems plays an important role in science,engineering and in the everyday life in general for handling approximate data. In the present paper grey numbers are used as a tool for assessing with linguistic expressions the mean performance of a group of objects participating in a certain activity. Two applications to student and football player assessment are also presented illustrating our results.
SINet: A Scale-insensitive Convolutional Neural Network for Fast Vehicle Detection
Venue: arXiv cs.CV 2018
Year: 2018

Vision-based vehicle detection approaches achieve incredible success in recent years with the development of deep convolutional neural network (CNN). However, existing CNN based algorithms suffer from the problem that the convolutional features are scale-sensitive in object detection task but it is common that traffic images and videos contain vehicles with a large variance of scales. In this paper, we delve into the source of scale sensitivity, and reveal two key issues: 1) existing RoI pooling destroys the structure of small scale objects, 2) the large intra-class distance for a large variance of scales exceeds the representation capability of a single network. Based on these findings, we present a scale-insensitive convolutional neural network (SINet) for fast detecting vehicles with a large variance of scales. First, we present a context-aware RoI pooling to maintain the contextual information and original structure of small scale objects. Second, we present a multi-branch decision network to minimize the intra-class distance of features. These lightweight techniques bring zero extra time complexity but prominent detection accuracy improvement. The proposed techniques can be equipped with any deep network architectures and keep them trained end-to-end. Our SINet achieves state-of-the-art performance in terms of accuracy and speed (up to 37 FPS) on the KITTI benchmark and a new highway dataset, which contains a large variance of scales and extremely small objects.
A neural network memory prefetcher using semantic locality
Venue: arXiv cs.DC 2018
Year: 2018

Accurate memory prefetching is paramount for processor performance, and modern processors employ various techniques to identify and prefetch different memory access patterns. While most modern prefetchers target spatio-temporal patterns by matching memory addresses that are accessed in close proximity (either in space or time), the recently proposed concept of semantic locality views locality as an artifact of the algorithmic level and searches for correlations between memory accesses and program state. While this approach was shown to be effective, capturing semantic locality requires significant associative learning capabilities. In this paper we utilize neural networks for this task. We leverage recent advances in machine learning to propose a neural network prefetcher. We show that by observing program context, this prefetcher can learn distinct memory access patterns that cannot be covered by other state-of-the-art prefetchers. We evaluate the neural network prefetcher over SPEC2006, Graph500, and several microbenchmarks. We show that the prefetcher can deliver an average speedup of 30% for SPEC2006 (up to 2.7x) and up to 4.6x over kernels. We also present a high-level design of our prefetcher, explore the power, energy and area limitations, and propose several optimizations for feasibility. We believe that this line of research can further improve the efficiency of such neural networks and allow harnessing them for additional micro-architectural predictions.
Real Time Sentiment Change Detection of Twitter Data Streams
Venue: arXiv cs.CL 2018
Year: 2018

In the past few years, there has been a huge growth in Twitter sentiment analysis having already provided a fair amount of research on sentiment detection of public opinion among Twitter users. Given the fact that Twitter messages are generated constantly with dizzying rates, a huge volume of streaming data is created, thus there is an imperative need for accurate methods for knowledge discovery and mining of this information. Although there exists a plethora of twitter sentiment analysis methods in the recent literature, the researchers have shifted to real-time sentiment identification on twitter streaming data, as expected. A major challenge is to deal with the Big Data challenges arising in Twitter streaming applications concerning both Volume and Velocity. Under this perspective, in this paper, a methodological approach based on open source tools is provided for real-time detection of changes in sentiment that is ultra efficient with respect to both memory consumption and computational cost. This is achieved by iteratively collecting tweets in real time and discarding them immediately after their process. For this purpose, we employ the Lexicon approach for sentiment characterizations, while change detection is achieved through appropriate control charts that do not require historical information. We believe that the proposed methodology provides the trigger for a potential large-scale monitoring of threads in an attempt to discover fake news spread or propaganda efforts in their early stages. Our experimental real-time analysis based on a recent hashtag provides evidence that the proposed approach can detect meaningful sentiment changes across a hashtags lifetime.
Semantic Adversarial Examples
Venue: arXiv cs.CV 2018
Year: 2018

Deep neural networks are known to be vulnerable to adversarial examples, i.e., images that are maliciously perturbed to fool the model. Generating adversarial examples has been mostly limited to finding small perturbations that maximize the model prediction error. Such images, however, contain artificial perturbations that make them somewhat distinguishable from natural images. This property is used by several defense methods to counter adversarial examples by applying denoising filters or training the model to be robust to small perturbations. In this paper, we introduce a new class of adversarial examples, namely "Semantic Adversarial Examples," as images that are arbitrarily perturbed to fool the model, but in such a way that the modified image semantically represents the same object as the original image. We formulate the problem of generating such images as a constrained optimization problem and develop an adversarial transformation based on the shape bias property of human cognitive system. In our method, we generate adversarial images by first converting the RGB image into the HSV (Hue, Saturation and Value) color space and then randomly shifting the Hue and Saturation components, while keeping the Value component the same. Our experimental results on CIFAR10 dataset show that the accuracy of VGG16 network on adversarial color-shifted images is 5.7%.
Generalizability vs. Robustness: Adversarial Examples for Medical Imaging
Venue: arXiv cs.CV 2018
Year: 2018

In this paper, for the first time, we propose an evaluation method for deep learning models that assesses the performance of a model not only in an unseen test scenario, but also in extreme cases of noise, outliers and ambiguous input data. To this end, we utilize adversarial examples, images that fool machine learning models, while looking imperceptibly different from original data, as a measure to evaluate the robustness of a variety of medical imaging models. Through extensive experiments on skin lesion classification and whole brain segmentation with state-of-the-art networks such as Inception and UNet, we show that models that achieve comparable performance regarding generalizability may have significant variations in their perception of the underlying data manifold, leading to an extensive performance gap in their robustness.
Entrenamiento de una red neuronal para el reconocimiento de imagenes de lengua de senas capturadas con sensores de profundidad
Venue: arXiv cs.CV 2018
Year: 2018

Due to the growth of the population with hearing problems, devices have been developed that facilitate the inclusion of deaf people in society, using technology as a communication tool, such as vision systems. Then, a solution to this problem is presented using neural networks and autoencoders for the classification of American Sign Language images. As a result, 99.5% accuracy and an error of 0.01684 were obtained for image classification
Cooperative Localization in Visible Light Networks: Theoretical Limits and Distributed Algorithms
Venue: arXiv cs.IT 2018
Year: 2018

Light emitting diode (LED) based visible light positioning (VLP) networks can provide accurate location information in indoor environments. In this manuscript, we propose to employ cooperative localization for visible light networks by designing a VLP system configuration that involves multiple LED transmitters with known locations (e.g., on the ceiling) and visible light communication (VLC) units equipped with both LEDs and photodetectors (PDs) for the purpose of cooperation. First, we derive the Cramér-Rao lower bound (CRLB) and the maximum likelihood estimator (MLE) for the localization of VLC units in the proposed cooperative scenario. To tackle the nonconvex structure of the MLE, we adopt a set-theoretic approach by formulating the problem of cooperative localization as a quasiconvex feasibility problem, where the aim is to find a point inside the intersection of convex constraint sets constructed as the sublevel sets of quasiconvex functions resulting from the Lambertian formula. Next, we devise two feasibility-seeking algorithms based on iterative gradient projections to solve the feasibility problem. Both algorithms are amenable to distributed implementation, thereby avoiding high-complexity centralized approaches. Capitalizing on the concept of quasi-Fejér convergent sequences, we carry out a formal convergence analysis to prove that the proposed algorithms converge to a solution of the feasibility problem in the consistent case. Numerical examples illustrate the improvements in localization performance achieved via cooperation among VLC units and evidence the convergence of the proposed algorithms to true VLC unit locations in both the consistent and inconsistent cases.
Image Compression Using Proposed Enhanced Run Length Encoding Algorithm
Venue: arXiv cs.MM 2018
Year: 2018

In this paper, we will present p roposed enhance process of image compression by using RLE algorithm. This proposed yield to decrease the size of compressing image, but the original method used primarily for compressing a binary images [1].Which will yield increasing the size of an original image mostly when used for color images. The test of an enhanced algorithm is performed on sample consists of ten BMP 24-bit true color images, building an application by using visual basic 6.0 to show the size after and before compression process and computing the compression ratio for RLE and for the enhanced RLE algorithm.
BATMAN : plate-forme blockchain pour l'authentification et la confiance dans les WSNs
Venue: arXiv cs.CR 2018
Year: 2018

Wireless Sensor networks (WSN) today suffer from a lack of security adapted to their multiple constraints, to which authentication and trust management solutions such as PGP only partially responds. On the one hand, the constraints of autonomy and co-operation of the nodes necessary to guarantee the coherence of the network do not require a distributed solution. On the other hand, the constraints of energy consumption and the low computing power of the nodes require the use of algorithms of low complexity (Zhang2014) . To our knowledge, no solution can answer both these problems at the same time. We are proposing a new solution for securing WSNs named BATMAN (Blockchain Authentication and Trust Module in Ad-hoc Networks) to reply to these challenges. We present a model of centralized management for authentication and trust, implementable on the Tezos blockchain, and evaluate through simulation the confidence estimators proposed here.
Algebraic matching techniques for fast decoding of polar codes with Reed-Solomon kernel
Venue: arXiv cs.IT 2018
Year: 2018

We propose to reduce the decoding complexity of polar codes with non-Arikan kernels by employing a (near) ML decoding algorithm for the codes generated by kernel rows. A generalization of the order statistics algorithm is presented for soft decoding of Reed-Solomon codes. Algebraic properties of the Reed-Solomon code are exploited to increase the reprocessing order. The obtained algorithm is used as a building block to obtain a decoder for polar codes with Reed-Solomon kernel.
Why Software Effort Estimation Needs SBSE
Venue: arXiv cs.SE 2018
Year: 2018

Industrial practitioners now face a bewildering array of possible configurations for effort estimation. How to select the best one for a particular dataset? This paper introduces OIL (short for optimized learning), a novel configuration tool for effort estimation based on differential evolution. When tested on 945 software projects, OIL significantly improved effort estimations, after exploring just a few configurations (just a few dozen). Further OIL's results are far better than two methods in widespread use: estimation-via-analogy and a recent state-of-the-art baseline published at TOSEM'15 by Whigham et al. Given that the computational cost of this approach is so low, and the observed improvements are so large, we conclude that SBSE should be a standard component of software effort estimation.
A Complete Model for Modular Simulation of Data Centre Power Load
Venue: arXiv cs.DC 2018
Year: 2018

Data centres are very fast growing structures with significant contribution to the world's energy consumption. Reducing the energy consumption of data centres is easier when the components that comprise a data centre and their respective energy consumption are known. A complete model for a modular design of a data centre and a technique for simulating each module's energy consumption are presented. Detailed power consumption modelling for each component as well as their interactions are the merits of this model. Unlike existing research, the present modular simulation model can take different design structures of data centres into account and provide us with hourly power consumption profiles for each component. The impacts of environmental parameters such as temperature and humidity are also investigated and incorporated into the model. The flexibility, scalability, comprehensiveness and modularity of this model provides researchers and designers with a powerful tool for energy analysis, management and planning of data centres with different designs and locations.
Minimizing Content Staleness in Dynamo-Style Replicated Storage Systems
Venue: arXiv cs.DC 2018
Year: 2018

Consistency in data storage systems requires any read operation to return the most recent written version of the content. In replicated storage systems, consistency comes at the price of delay due to large-scale write and read operations. Many applications with low latency requirements tolerate data staleness in order to provide high availability and low operation latency. Using age of information as the staleness metric, we examine a data updating system in which real-time content updates are replicated and stored in a Dynamo-style quorum- based distributed system. A source sends updates to all the nodes in the system and waits for acknowledgements from the earliest subset of nodes, known as a write quorum. An interested client fetches the update from another set of nodes, defined as a read quorum. We analyze the staleness-delay tradeoff in replicated storage by varying the write quorum size. With a larger write quorum, an instantaneous read is more likely to get the latest update written by the source. However, the age of the content written to the system is more likely to become stale as the write quorum size increases. For shifted exponential distributed write delay, we derive the age optimized write quorum size that balances the likelihood of reading the latest update and the freshness of the latest update written by the source.
Automatic Web Security Unit Testing: XSS Vulnerability Detection
Venue: arXiv cs.CR 2018
Year: 2018

Integrating security testing into the workflow of software developers not only can save resources for separate security testing but also reduce the cost of fixing security vulnerabilities by detecting them early in the development cycle. We present an automatic testing approach to detect a common type of Cross Site Scripting (XSS) vulnerability caused by improper encoding of untrusted data. We automatically extract encoding functions used in a web application to sanitize untrusted inputs and then evaluate their effectiveness by automatically generating XSS attack strings. Our evaluations show that this technique can detect 0-day XSS vulnerabilities that cannot be found by static analysis tools. We will also show that our approach can efficiently cover a common type of XSS vulnerability. This approach can be generalized to test for input validation against other types injections such as command line injection.
Learning-Based Task Offloading for Vehicular Cloud Computing Systems
Venue: arXiv cs.IT 2018
Year: 2018

Vehicular cloud computing (VCC) is proposed to effectively utilize and share the computing and storage resources on vehicles. However, due to the mobility of vehicles, the network topology, the wireless channel states and the available computing resources vary rapidly and are difficult to predict. In this work, we develop a learning-based task offloading framework using the multi-armed bandit (MAB) theory, which enables vehicles to learn the potential task offloading performance of its neighboring vehicles with excessive computing resources, namely service vehicles (SeVs), and minimizes the average offloading delay. We propose an adaptive volatile upper confidence bound (AVUCB) algorithm and augment it with load-awareness and occurrence-awareness, by redesigning the utility function of the classic MAB algorithms. The proposed AVUCB algorithm can effectively adapt to the dynamic vehicular environment, balance the tradeoff between exploration and exploitation in the learning process, and converge fast to the optimal SeV with theoretical performance guarantee. Simulations under both synthetic scenario and a realistic highway scenario are carried out, showing that the proposed algorithm achieves close-to-optimal delay performance.
Full Characterization of Optimal Uncoded Placement for the Structured Clique Cover Delivery of Nonuniform Demands
Venue: arXiv cs.IT 2018
Year: 2018

We investigate the problem of coded caching for nonuniform demands when the structured clique cover algorithm proposed by Maddah-Ali and Niesen for decentralized caching is used for delivery. We apply this algorithm to all user demands regardless of their request probabilities. This allows for coding among the files that have different request probabilities but makes the allocation of memory to different files challenging during the content placement phase. As our main contribution, we analytically characterize the optimal placement strategy that minimizes the expected delivery rate under a storage capacity constraint. It is shown that the optimal placement follows either a two or a three group strategy, where a set of less popular files are not cached at all and the files within each of the other sets are allocated identical amounts of storage as if they had the same request probabilities. We show that for a finite set of storage capacities, that we call the base-cases of the problem, the two group strategy is always optimal. For other storage capacities, optimal placement is achieved by memory sharing between certain base-cases and the resulting placement either follows a two or a three group strategy depending on the corresponding base-cases used. We derive a polynomial time algorithm that determines the base-cases of the problem given the number of caches and popularity distribution of files. Given the base-cases of the problem, the optimal memory allocation parameters for any storage capacity are derived analytically.
Convolutional Neural Networks Regularized by Correlated Noise
Venue: arXiv cs.LG 2018
Year: 2018

Neurons in the visual cortex are correlated in their variability. The presence of correlation impacts cortical processing because noise cannot be averaged out over many neurons. In an effort to understand the functional purpose of correlated variability, we implement and evaluate correlated noise models in deep convolutional neural networks. Inspired by the cortex, correlation is defined as a function of the distance between neurons and their selectivity. We show how to sample from high-dimensional correlated distributions while keeping the procedure differentiable, so that back-propagation can proceed as usual. The impact of correlated variability is evaluated on the classification of occluded and non-occluded images with and without the presence of other regularization techniques, such as dropout. More work is needed to understand the effects of correlations in various conditions, however in 10/12 of the cases we studied, the best performance on occluded images was obtained from a model with correlated noise.